Process control systems are commonly used to control industrial systems such as chemical reactors, distillation columns, pulp digesters, power-generation plants, etc. Innovations in control-system technology, including distributed control systems and programmable logic controllers, have increased the complexity of process control systems used in such applications. Contemporary process control systems can operate with dozens, or in some cases, hundreds of inputs and control loops.
The performance of each individual control loop of a multi-loop process control system needs to be optimized, or “tuned,” to achieve optimum overall performance in the control system. The individual control loops, however, often interact with other control loops. Hence, changes in the performance of one loop (both positive and negative) can adversely affect the performance of other loops in the control system.
The performance of a multi-loop control system typically changes as both the control system and the process being controlled age. For example, pneumatic components of a control system can lose their effectiveness over time due to air leaks, and corrosion on electrical contacts and wiring can degrade the effectiveness of electronic components. Moreover, the above-noted interaction between control loops in a multi-loop system can multiply the adverse effects associated with the degradation of a single control loop.
Performance degradation in a process control system can adversely affect the process being controlled by the system. Hence, the performance of process control systems is often monitored to determine when corrective action such as maintenance, repair, or retuning is needed.
Most current techniques for monitoring the performance of multi-loop process control systems analyze each controlled parameter on an individual basis. In other words, each control loop is usually considered in isolation from the other loops, and a single representation of the overall performance of the system typically is not available. Hence, it can be difficult to monitor the overall performance of the process control system, and to compare the overall performance with a benchmark level to determine when corrective action such as maintenance or repairs is required. Furthermore, typical monitoring techniques do not account for the effects of different operating conditions on the performance of the individual control loops, or for the relative importance of certain loops with respect to factors such as economic impact.